AutoML Leaderboard

Best model	name	model_type	metric_type	metric_value	train_time
	1_Baseline	Baseline	auc	0.5	0.91
	2_DecisionTree	Decision Tree	auc	0.502156	6.59
	3_Linear	Linear	auc	0.631282	5.02
	4_Default_Xgboost	Xgboost	auc	0.613589	20.4
	5_Default_NeuralNetwork	Neural Network	auc	0.604687	2.3
	6_Default_RandomForest	Random Forest	auc	0.578397	6.13
the best	Ensemble	Ensemble	auc	0.634217	0.48

AutoML Performance

AutoML Performance Boxplot

Features Importance

features importance across models

Spearman Correlation of Models

models spearman correlation

Summary of 5_Default_NeuralNetwork

<< Go back

Neural Network

n_jobs: -1
dense_1_size: 32
dense_2_size: 16
learning_rate: 0.05
explain_level: 2

Validation

validation_type: split
train_ratio: 0.75
shuffle: True
stratify: True

Optimized metric

auc

Training time

1.5 seconds

Metric details

	score	threshold
logloss	0.728984	nan
auc	0.604687	nan
f1	0.651748	0.0937592
accuracy	0.582	0.517806
precision	0.693878	0.716811
recall	1	0.0071289
mcc	0.172976	0.313494

Metric details with threshold from accuracy metric

	score	threshold
logloss	0.728984	nan
auc	0.604687	nan
f1	0.490244	0.517806
accuracy	0.582	0.517806
precision	0.596439	0.517806
recall	0.416149	0.517806
mcc	0.161846	0.517806

Confusion matrix (at threshold=0.517806)

	Predicted as 0	Predicted as 1
Labeled as 0	381	136
Labeled as 1	282	201

Learning curves

Permutation-based Importance

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

<< Go back

Summary of 1_Baseline

<< Go back

Baseline Classifier (Baseline)

n_jobs: -1
explain_level: 2

Validation

validation_type: split
train_ratio: 0.75
shuffle: True
stratify: True

Optimized metric

auc

Training time

0.2 seconds

Metric details

	score	threshold
logloss	0.692569	nan
auc	0.5	nan
f1	0.651382	0.435
accuracy	0.483	0.435
precision	0.483	0.435
recall	1	0.435
mcc	0	0.435

Metric details with threshold from accuracy metric

	score	threshold
logloss	0.692569	nan
auc	0.5	nan
f1	0.651382	0.435
accuracy	0.483	0.435
precision	0.483	0.435
recall	1	0.435
mcc	0	0.435

Confusion matrix (at threshold=0.435)

	Predicted as 0	Predicted as 1
Labeled as 0	0	517
Labeled as 1	0	483

Learning curves

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

<< Go back

Summary of Ensemble

<< Go back

Ensemble structure

Model	Weight
3_Linear	3
4_Default_Xgboost	1

Metric details

	score	threshold
logloss	0.665976	nan
auc	0.634217	nan
f1	0.663205	0.318661
accuracy	0.598	0.562257
precision	0.85	0.759777
recall	1	0.100798
mcc	0.202265	0.423461

Metric details with threshold from accuracy metric

	score	threshold
logloss	0.665976	nan
auc	0.634217	nan
f1	0.491139	0.562257
accuracy	0.598	0.562257
precision	0.631922	0.562257
recall	0.401656	0.562257
mcc	0.198355	0.562257

Confusion matrix (at threshold=0.562257)

	Predicted as 0	Predicted as 1
Labeled as 0	404	113
Labeled as 1	289	194

Learning curves

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

<< Go back

Summary of 2_DecisionTree

<< Go back

Decision Tree

n_jobs: -1
criterion: gini
max_depth: 3
explain_level: 2

Validation

validation_type: split
train_ratio: 0.75
shuffle: True
stratify: True

Optimized metric

auc

Training time

5.7 seconds

Metric details

	score	threshold
logloss	0.703887	nan
auc	0.502156	nan
f1	0.651382	0.331967
accuracy	0.519	0.59996
precision	0.522727	0.59996
recall	1	0.331967
mcc	0.0349949	0.368852

Metric details with threshold from accuracy metric

	score	threshold
logloss	0.703887	nan
auc	0.502156	nan
f1	0.0872865	0.59996
accuracy	0.519	0.59996
precision	0.522727	0.59996
recall	0.047619	0.59996
mcc	0.0170556	0.59996

Confusion matrix (at threshold=0.59996)

	Predicted as 0	Predicted as 1
Labeled as 0	496	21
Labeled as 1	460	23

Learning curves

Permutation-based Importance

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

SHAP Importance

<< Go back

Summary of 6_Default_RandomForest

<< Go back

Random Forest

n_jobs: -1
criterion: gini
max_features: 0.9
min_samples_split: 30
max_depth: 4
eval_metric_name: auc
explain_level: 2

Validation

validation_type: split
train_ratio: 0.75
shuffle: True
stratify: True

Optimized metric

auc

Training time

5.2 seconds

Metric details

	score	threshold
logloss	0.683715	nan
auc	0.578397	nan
f1	0.651994	0.395058
accuracy	0.573	0.474979
precision	0.6	0.566713
recall	1	0.300369
mcc	0.151472	0.474979

Metric details with threshold from accuracy metric

	score	threshold
logloss	0.683715	nan
auc	0.578397	nan
f1	0.591388	0.474979
accuracy	0.573	0.474979
precision	0.549822	0.474979
recall	0.639752	0.474979
mcc	0.151472	0.474979

Confusion matrix (at threshold=0.474979)

	Predicted as 0	Predicted as 1
Labeled as 0	264	253
Labeled as 1	174	309

Learning curves

Permutation-based Importance

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

SHAP Importance

<< Go back

Summary of 4_Default_Xgboost

<< Go back

Extreme Gradient Boosting (Xgboost)

n_jobs: -1
objective: binary:logistic
eta: 0.075
max_depth: 6
min_child_weight: 1
subsample: 1.0
colsample_bytree: 1.0
eval_metric: auc
explain_level: 2

Validation

validation_type: split
train_ratio: 0.75
shuffle: True
stratify: True

Optimized metric

auc

Training time

19.4 seconds

Metric details

	score	threshold
logloss	0.67694	nan
auc	0.613589	nan
f1	0.651382	0.103189
accuracy	0.597	0.510386
precision	0.826087	0.731419
recall	1	0.103189
mcc	0.191162	0.510386

Metric details with threshold from accuracy metric

	score	threshold
logloss	0.67694	nan
auc	0.613589	nan
f1	0.555678	0.510386
accuracy	0.597	0.510386
precision	0.59434	0.510386
recall	0.521739	0.510386
mcc	0.191162	0.510386

Confusion matrix (at threshold=0.510386)

	Predicted as 0	Predicted as 1
Labeled as 0	345	172
Labeled as 1	231	252

Learning curves

Permutation-based Importance

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

SHAP Importance

<< Go back

Summary of 3_Linear

<< Go back

Logistic Regression (Linear)

n_jobs: -1
explain_level: 2

Validation

validation_type: split
train_ratio: 0.75
shuffle: True
stratify: True

Optimized metric

auc

Training time

4.1 seconds

Metric details

	score	threshold
logloss	0.669239	nan
auc	0.631282	nan
f1	0.657728	0.246618
accuracy	0.6	0.534127
precision	0.8	0.800797
recall	1	0.0914683
mcc	0.19974	0.390411

Metric details with threshold from accuracy metric

	score	threshold
logloss	0.669239	nan
auc	0.631282	nan
f1	0.531616	0.534127
accuracy	0.6	0.534127
precision	0.61186	0.534127
recall	0.469979	0.534127
mcc	0.198043	0.534127

Confusion matrix (at threshold=0.534127)

	Predicted as 0	Predicted as 1
Labeled as 0	373	144
Labeled as 1	256	227

Learning curves

Coefficients

feature	Learner_1
feature_35	0.200405
feature_20	0.192239
feature_15	0.191565
intercept	0.146226
feature_40	0.138789
feature_33	0.135812
feature_43	0.134685
feature_66	0.130875
feature_4	0.130841
feature_12	0.126476
feature_10	0.119604
feature_49	0.118894
feature_14	0.107123
feature_19	0.104659
feature_46	0.0949958
feature_58	0.0940737
feature_71	0.0897494
feature_53	0.0893205
feature_68	0.0839031
feature_42	0.0813232
feature_25	0.0804899
feature_69	0.0795484
feature_2	0.0768902
feature_73	0.0739344
feature_22	0.0732772
feature_21	0.0710029
feature_39	0.0676263
feature_54	0.0670369
feature_36	0.0655498
feature_16	0.0655498
feature_63	0.0621283
feature_32	0.0602608
feature_44	0.0571233
feature_41	0.0553329
feature_24	0.0404095
feature_56	0.0297897
feature_17	0.0272678
feature_23	0.0191304
feature_3	-0.00117303
feature_27	-0.0141339
feature_18	-0.0175461
feature_38	-0.0220414
feature_8	-0.028008
feature_62	-0.0331552
feature_76	-0.0389717
feature_51	-0.0448625
feature_13	-0.047958
feature_50	-0.0490565
feature_75	-0.0547078
feature_74	-0.0570883
feature_31	-0.059011
feature_52	-0.0665689
feature_61	-0.0693664
feature_47	-0.0745467
feature_7	-0.0761572
feature_72	-0.0790566
feature_1	-0.0802046
feature_26	-0.0817932
feature_65	-0.0830271
feature_59	-0.0879831
feature_48	-0.0910561
feature_34	-0.0992595
feature_28	-0.102777
feature_30	-0.103224
feature_45	-0.116688
feature_64	-0.117451
feature_37	-0.118839
feature_67	-0.123572
feature_11	-0.13637
feature_55	-0.138853
feature_57	-0.172999
feature_9	-0.203542
feature_29	-0.206588
feature_5	-0.21696
feature_70	-0.22905
feature_6	-0.234133
feature_60	-0.241553